Articles | Volume 12, issue 3
https://doi.org/10.5194/essd-12-2209-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-12-2209-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
15 Sep 2020
Data description paper |
| 15 Sep 2020
| 15 Sep 2020
DSCOVR/EPIC-derived global hourly and daily downward shortwave and photosynthetically active radiation data at 0.1° × 0.1° resolution
Dalei Hao
Joint Global Change Research Institute, Pacific Northwest National
Laboratory, College Park, MD 20740, USA
State Key Laboratory of Remote Sensing Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy of Sciences, Beijing 100049, China
Ghassem R. Asrar
Universities Space Research Association, Columbia, MD 21046, USA
Yelu Zeng
Joint Global Change Research Institute, Pacific Northwest National
Laboratory, College Park, MD 20740, USA
Qing Zhu
Earth Science Division, Lawrence Berkeley National Lab, Berkeley, CA 94720, USA
Jianguang Wen
State Key Laboratory of Remote Sensing Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy of Sciences, Beijing 100049, China
Qing Xiao
State Key Laboratory of Remote Sensing Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy of Sciences, Beijing 100049, China
Joint Global Change Research Institute, Pacific Northwest National
Laboratory, College Park, MD 20740, USA
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23 citations as recorded by crossref.
- An alternative cloud index for estimating downwelling surface solar irradiance from various satellite imagers in the framework of a Heliosat-V method B. Tournadre et al. 10.5194/amt-15-3683-2022
- WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry–meteorology interactions X. Feng et al. 10.5194/gmd-14-3741-2021
- Potential of hotspot solar‐induced chlorophyll fluorescence for better tracking terrestrial photosynthesis D. Hao et al. 10.1111/gcb.15554
- Lagrange Point Missions: The Key to next Generation Integrated Earth Observations. DSCOVR Innovation F. Valero et al. 10.3389/frsen.2021.745938
- Time-series variation and attribution analysis of downward shortwave radiation over the Yunnan-Kweichow plateau from 1984 to 2018 L. Cheng et al. 10.1080/17538947.2023.2174194
- Machine Learning and Physics-Based Hybridization Models for Evaluation of the Effects of Climate Change and Urban Expansion on Photosynthetically Active Radiation S. Nwokolo et al. 10.3390/atmos14040687
- Structural complexity biases vegetation greenness measures Y. Zeng et al. 10.1038/s41559-023-02187-6
- Worldwide validation of an Earth Polychromatic Imaging Camera (EPIC) derived radiation product and comparison with recent reanalyses X. Yang et al. 10.1016/j.solener.2022.08.013
- Evaluating Topographic Effects on Kilometer-Scale Satellite Downward Shortwave Radiation Products: A Case Study in Mid-Latitude Mountains Y. Ma et al. 10.1109/TGRS.2024.3365865
- Predicting Underwater Photosynthetically Active Radiation (PAR) Using Random Forests in the São Sebastião Channel, Brazil H. Foloni-Neto & Á. Ciotti 10.1007/s12601-024-00163-0
- Using supervised learning to develop BaRAD, a 40-year monthly bias-adjusted global gridded radiation dataset T. Chakraborty & X. Lee 10.1038/s41597-021-01016-4
- A GeoNEX-based high-spatiotemporal-resolution product of land surface downward shortwave radiation and photosynthetically active radiation R. Li et al. 10.5194/essd-15-1419-2023
- Comprehensive gridded dataset of photosynthetically active radiation in the upper ocean from 1958 to 2022 J. Castant et al. 10.1016/j.rse.2024.114305
- Retrieval of surface solar irradiance from satellite imagery using machine learning: pitfalls and perspectives H. Verbois et al. 10.5194/amt-16-4165-2023
- A model for urban biogenic CO<sub>2</sub> fluxes: Solar-Induced Fluorescence for Modeling Urban biogenic Fluxes (SMUrF v1) D. Wu et al. 10.5194/gmd-14-3633-2021
- A dense station-based, long-term and high-accuracy dataset of daily surface solar radiation in China W. Tang et al. 10.5194/essd-15-4537-2023
- Adjusting solar-induced fluorescence to nadir-viewing provides a better proxy for GPP D. Hao et al. 10.1016/j.isprsjprs.2022.01.016
- Diurnal Cycle in Surface Incident Solar Radiation Characterized by CERES Satellite Retrieval L. Lu & Q. Ma 10.3390/rs15133217
- Constructing a long-term global dataset of direct and diffuse radiation (10 km, 3 h, 1983–2018) separating from the satellite-based estimates of global radiation W. Tang et al. 10.1016/j.rse.2024.114292
- Impacts of climate change and meteo-solar parameters on photosynthetically active radiation prediction using hybrid machine learning with Physics-based models S. Chukwujindu Nwokolo et al. 10.1016/j.asr.2022.08.010
- Tracking diurnal to seasonal variations of gross primary productivity using a geostationary satellite, GK-2A advanced meteorological imager S. Jeong et al. 10.1016/j.rse.2022.113365
- Comprehensive assessment of five global daily downward shortwave radiation satellite products R. Li et al. 10.1016/j.srs.2021.100028
- Validation and calibration of models to estimate photosynthetically active radiation considering different time scales and sky conditions M. de Blas et al. 10.1016/j.asr.2022.07.005
Latest update: 22 Nov 2024
Short summary
We adopted machine-learning models to generate the first global land products of SW–PAR based on DSCOVR/EPIC data. Our products are consistent with ground-based observations, capture the spatiotemporal patterns well and accurately track substantial diurnal, monthly and seasonal variations in SW–PAR. Our products provide a valuable alternative for solar photovoltaic applications and can be used to improve our understanding of the diurnal cycles of terrestrial water, carbon and energy fluxes.
We adopted machine-learning models to generate the first global land products of SW–PAR based on...
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